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Is investing time in optimizing your MySQL database worth the performance benefits that you gain as a result? The answer is an unequivocal “yes”! Many Linux-Apache-MySQL-PHP (LAMP) developers struggle with the question of how to optimize their MySQL queries to improve load times and database efficiency. This guide provides an overview for evaluating the processes involved in fine-tuning your SQL queries. To start, you will need to benchmark your current database performance using a common open source tool such as SysBench. This will help you understand your current performance pitfalls as well set achievable goals on how to deal with them.

Utilizing Data to Understand DB Performance
Benchmarking your database is a good first step towards determining the overall health of your database. Profiling is the next step that will reveal the detailed opportunities for improvement. By profiling your database you can gain insights into which queries indicate slow response time in your server logs. While aggregate performance of your database can show overall metrics, several MySQL commands such as “show processlist” can provide detailed insights into potential bottlenecks. Analyzing raw database and server logs can take hours of filtering; database performance tools can provide deeper insights into where you should focus your optimization efforts.

Many developers are surprised which queries are slowing load times, since often multiple join-queries are actually more efficient than simple syntax depending on the overall application design. As a rule of thumb, always judge databases based upon measured data instead of trying to hypothesize where the problems are emerging. Developing and testing in a data-driven environment with respect to database performance is as important as testing your overall code – being able to scale your application requires maintaining efficient database performance from beginning to end.

Optimizing Database Structure to Improve Performance
One optimization opportunity that many developers overlook is the relation of queries to your overall database structure. The overall schema and structure of your database can impact the performance of individual data queries. Designing your database for optimal efficiency will ensure you can maximize the performance of your site as it scales.

When evaluating the overall structure of your database, it is important to analyze the frequency with which you access certain columns. Smart portions of your tables can help by creating sub-tables for frequent entries such as user status on a social network or more popular posts on a blog site.

How you index tables is also important, since large scale databases are like looking for a sentence in a novel unless you properly index against certain entries. By grouping indices logically you can improve query lookup times as well as design your database to scale more effectively. At the same time you should select index options carefully since they utilize memory. An optimal selection of index options can result in improved database performance.

One area where you can help make structural improvements is in your choice of the writer database storage solution. Whether you utilize an out of the box MySQL solution (which provides MyISAM, InnoDB, MERGE and MEMORY storage engines) or a third-party database engine, you should always evaluate which configuration works best for your specific needs. If you are constantly writing to the database, for example, InnoDB may deliver superior performance, helping you scale to meet user needs over time.

As with any computational optimization, there are tradeoffs to making simplifications to your database structure so always think in terms of the long run implications before making individual updates. Evaluating database structures for performance can make a demonstrable difference when it comes to scaling your application more effectively.